Processing system for performing predictive error resolution and dynamic system configuration control

ABSTRACT

Aspects of the disclosure relate to error resolution processing systems with improved error prediction features and enhanced resolution techniques. A computing platform may receive error log files identifying error codes corresponding to error occurrences on one or more different virtual machine host platforms. The computing platform may aggregate the error codes corresponding to the error occurrences to generate an error lattice. Using the error lattice, the computing platform may predict an error outcome. Based on the predicted error outcome, the computing platform may determine a system configuration update to be applied to the one or more virtual machine host platforms. The computing platform may direct a dynamic resource management computing platform to distribute relevant portions of the system configuration update to each of the one or more virtual machine host platforms. This may cause the one or more virtual machine host platforms to implement the system configuration update.

BACKGROUND

Aspects of the disclosure relate to enhanced processing systems forperforming predictive error resolution and dynamic system configurationcontrol. In particular, one or more aspects of the disclosure relate topredictive error resolution systems and dynamic system configurationcontrol systems that utilize one or more error log files to performerror prediction, improve error resolution, and facilitate systemconfiguration updates.

Because many organizations and individuals rely on electronic transfersas a method for exchanging secure data records, it may be important topredict potential errors in such transfers, adjust system configurationsto avoid the potential errors, and efficiently resolve errors that doactually occur as a result of the electronic transfers. In manyinstances, however, it may be difficult to optimize technicalperformance and operating efficiency of the computing systems thatperform electronic transfers while also ensuring that errors areeffectively predicted, avoided to the maximum extent possible, andresolved when necessary.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with optimizing the performance of and ensuring theefficiency of predictive error resolution and dynamic systemconfiguration control computer systems.

In accordance with one or more embodiments of the disclosure, acomputing platform comprising at least one processor, a communicationinterface, and memory storing computing readable instructions mayreceive, from one or more virtual machine host platforms, one or moreerror log files identifying error codes corresponding to erroroccurrences associated with multiple applications running on the one ormore virtual machine host platforms. Based on one or more error logfiles, the computing platform may generate an error lattice comprisingan aggregation of the error codes corresponding to the erroroccurrences. Based on the error lattice, the computing platform mayidentify relationships between the error codes corresponding to theerror occurrences. Based on the relationships between the error codescorresponding to the error occurrences, the computing platform maydetermine a predicted error outcome. Based on the predicted erroroutcome, the computing platform may determine a system configurationupdate to be applied to the one or more virtual machine host platforms.The computing platform may generate one or more commands directing adynamic resource management computing platform to distribute relevantportions of the system configuration update to each of the one or morevirtual machine host platforms. The computing platform may send, to thedynamic resource management computing platform, one or more commandsdirecting the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the one or more virtual machine host platforms, wherein sendingthe one or more commands directing the dynamic resource managementcomputing platform to distribute the relevant portions of the systemconfiguration update to each of the one or more virtual machine hostplatforms causes the one or more virtual machine host platforms toimplement the system configuration update.

In some embodiments, the computing platform may establish, with a firstvirtual machine host platform of the one or more virtual machine hostplatforms, a first wireless data connection. The computing platform mayalso establish, with a second virtual machine host platform of the oneor more virtual machine host platforms, a second wireless dataconnection. The computing platform may establish, with the dynamicresource management computing platform, a third wireless dataconnection.

In some embodiments, the computing platform may receive the one or moreerror log files identifying the error codes corresponding to the erroroccurrences associated with multiple applications running on the one ormore virtual machine host platforms by receiving, via the communicationinterface, the one or more error log files identifying the error codescorresponding to the error occurrences associated with multipleapplications running on the one or more virtual machine host platforms.In some examples, the computing platform may send to the dynamicresource management computing platform the one or more commandsdirecting the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the one or more virtual machine host platforms by sending, viathe communication interface and to the dynamic resource managementcomputing platform, the one or more commands directing the dynamicresource management computing platform to distribute the relevantportions of the system configuration update to each of the one or morevirtual machine host platforms.

In some embodiments, the computing platform may determine, prior todetermining the system configuration update to be applied to the one ormore virtual machine host platforms, that the predicted error outcomeexceeds a predetermined error outcome threshold.

In some embodiments, the computing platform may identify, based on theone or more error log files, an actual error. After identifying theactual error, the computing platform may send error informationcorresponding to the actual error to the dynamic resource managementcomputing platform.

In some embodiments, the system configuration update comprisesincreasing a data capacity of at least one of the one or more virtualmachine host platforms.

In some embodiments, the computing platform may generate the errorlattice comprising an aggregation of the error codes corresponding tothe error occurrences by generating the error lattice in real time asthe one or more error log files identifying error codes corresponding toerror occurrences associated with multiple applications running on theone or more virtual machine host platforms are received.

In accordance with one or more additional embodiments of the disclosure,a computing platform comprising at least one processor, a communicationinterface, and memory storing computer readable instructions mayreceive, from a predictive error resolution computing platform, one ormore commands directing the computing platform to distribute relevantportions of a system configuration update. The computing platform mayidentify one or more virtual machine host platforms to which the systemconfiguration update is applicable. Based on the system configurationupdate, the computing platform may generate one or more commandsdirecting each of the one or more virtual machine host platforms towhich the system configuration update is applicable to perform systemupdates based on the system configuration update. The computing platformmay receive, from the one or more virtual machine host platforms, aconfiguration update confirmation notification. Based on at least onemachine learning algorithm and at least one dataset, the computingplatform may generate an error map identifying correlations betweenerror codes and a respective operator for each error code. The computingplatform may receive, from the predictive error resolution computingplatform, error information comprising a plurality of error codescorresponding to error occurrences. Based on the error map, thecomputing platform may determine operator interface informationindicating one or more operators associated with resolution of each ofthe plurality of error codes corresponding to the error occurrences. Thecomputing platform may generate one or more hand off commands for one ormore user devices, each hand off command of the one or more hand offcommands being associated with an operator of the one or more operators,to cause display of an operator interface associated with the respectiveoperator. Along with the one or more hand off commands, the computingplatform may send, to the one or more user devices, the operatorinterface information. Based on the error occurrences and using at leastone additional machine learning algorithm and at least one additionaldataset, the computing platform may generate an error correction hub.The computing platform may generate one or more commands directing aclient management computing platform to cause display of the errorcorrection hub. Along with the one or more commands directing the clientmanagement computing platform to cause display of the error correctionhub, the computing platform may send the error correction hub.

In some embodiments, the computing platform may establish, with thepredictive error resolution computing platform, each of the one or morevirtual machine host platforms, each of the one or more user devices,and the client management computing platform, wireless data connections.

In some embodiments, the computing platform may send, using thecommunication interface, using the wireless data connections, and to theone or more virtual machine host platforms to which the systemconfiguration update is applicable, the one or more commands directingeach of the one or more virtual machine host platforms to which thesystem configuration update is applicable to perform system updatesbased on the system configuration update.

In some embodiments, the computing platform may generate the errorcorrection hub by generating a user interface displaying one or more of:the error occurrences, the one or more operators associated withresolution of each of the plurality of error codes corresponding to theerror occurrences, a resolution method associated with resolution ofeach of the plurality of error codes corresponding to the erroroccurrences, and an estimated resolution time for conducting theresolution method associated with resolution of each of the plurality oferror codes corresponding to the error occurrences.

In some embodiments, the computing platform may determine, based on oneor more machine learning algorithms and one or more machine learningdatasets, the resolution method associated with resolution of each ofthe plurality of error codes corresponding to the error occurrences andthe estimated resolution time for conducting the resolution methodassociated with resolution of each of the plurality of error codescorresponding to the error occurrences.

In some embodiments, the computing platform may determine that the errormap does not identify the error information comprising a plurality oferror codes corresponding to error occurrences. The computing platformmay also update, using the plurality of error codes corresponding toerror occurrences and after determining operators corresponding to eachof the plurality of error codes corresponding to error occurrences, theerror map to identify the plurality of error codes and the operators.

In some embodiments, the computing platform may send to a second userdevice of the one or more user devices and after receiving an error coderesolution indication from a first user device of the one or more userdevices, the operator interface information.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A-1C depict an illustrative computing environment for deployingan enhanced processing system that utilizes improved error predictionand dynamic system configuration control techniques in accordance withone or more example embodiments;

FIGS. 2A-2L depict an illustrative event sequence for deploying anenhanced processing system that utilizes improved error prediction anddynamic system configuration control techniques in accordance with oneor more example embodiments;

FIGS. 3 and 4 depict example graphical user interfaces for deploying anenhanced processing system that utilizes improved error prediction anddynamic system configuration control techniques in accordance with oneor more example embodiments; and

FIGS. 5 and 6 depict an illustrative method for deploying an enhancedprocessing system that utilizes improved error prediction and dynamicsystem configuration control techniques in accordance with one or moreexample embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

FIGS. 1A-1C depict an illustrative computing environment for deploying aprocessing system for performing predictive error resolution and dynamicsystem configuration control that utilizes improved error resolutiontechniques in accordance with one or more example embodiments. Referringto FIG. 1A, computing environment 100 may include one or more computersystems. For example, computing environment 100 may include a predictiveerror resolution computing platform 102, a dynamic resource managementcomputing platform 103, a first virtual machine host platform 104, asecond virtual machine host platform 105, a first user device 106, asecond user device 107, and a client management computing platform 108.

As illustrated in greater detail below, predictive error resolutioncomputing platform 102 may include one or more computing devicesconfigured to perform one or more of the functions described herein. Forexample, predictive error resolution computing platform 102 may includeone or more computers (e.g., laptop computers, desktop computers,servers, server blades, or the like).

As illustrated in greater detail below, dynamic resource managementcomputing platform 103 may include one or more computing devicesconfigured to perform one or more of the functions described herein. Forexample, dynamic resource management computing platform 103 may includeone or more computers (e.g., laptop computers, desktop computers,servers, server blades, or the like).

First virtual machine host platform 104 may be a computer system thatincludes one or more computing devices and/or other computer components(e.g., processors, memories, communication interfaces). In addition,first virtual machine host platform 104 may be configured to receiverequests (e.g., requests to adjust a system configuration from a dynamicresource management computing platform, such as dynamic resourcemanagement computing platform 103, and the like.) In some instances, thefirst virtual machine host platform 104 may generate an error log. Thefirst virtual machine host platform 104 may send the error log to thepredictive error resolution computing platform 102 for further analysis,as discussed in greater detail below.

Second virtual machine host platform 105 may be a computer system thatincludes one or more computing devices and/or other computer components(e.g., processors, memories, communication interfaces). In addition,second virtual machine host platform 105 may be configured to receiverequests (e.g., requests to adjust a system configuration from a dynamicresource management computing platform, such as dynamic resourcemanagement computing platform 103, and the like.) In some instances, thesecond virtual machine host platform 105 may generate an error log. Thesecond virtual machine host platform 105 may send the error log to thepredictive error resolution computing platform 102 for further analysis,as discussed in greater detail below.

First user device 106 may include one or more computing devices and/orother computer components (e.g., processors, memories, communicationinterfaces). In addition, and as illustrated in greater detail below,first user device 106 may be configured to generate, host, transmit,and/or otherwise provide one or more web pages and/or other graphicaluser interfaces (which may, e.g., cause one or more other computersystems to display and/or otherwise present the one or more web pagesand/or other graphical user interfaces). In some instances, the webpages and/or other graphical user interfaces generated by first userdevice 106 may be associated with an internal portal provided by anorganization, such as an error management portal provided by a financialinstitution. Such a portal may, for instance, provide employees of thefinancial institution with access to error information (e.g., errorcodes, types of errors, resolution methods, employees responsible forvarious errors, error resolution times, or the like) and/or may provideemployees of the financial institution with menus, controls, and/orother options to execute various resolution actions (e.g., modify asystem configuration, redirecting the error to another employee,initiate load balancing, or the like).

Second user device 107 may include one or more computing devices and/orother computer components (e.g., processors, memories, communicationinterfaces). In addition, and as illustrated in greater detail below,second user device 107 may be configured to generate, host, transmit,and/or otherwise provide one or more web pages and/or other graphicaluser interfaces (which may, e.g., cause one or more other computersystems to display and/or otherwise present the one or more web pagesand/or other graphical user interfaces). In some instances, the webpages and/or other graphical user interfaces generated by second userdevice 107 may be associated with an internal portal provided by anorganization, such as an error management portal provided by a financialinstitution. Such a portal may, for instance, provide employees of thefinancial institution with access to error information (e.g., errorcodes, types of errors, resolution methods, employees responsible forvarious errors, error resolution times, or the like) and/or may provideemployees of the financial institution with menus, controls, and/orother options to execute various resolution actions (e.g., modify asystem configuration, redirecting the error to another employee,initiate load balancing, or the like).

Client management computing platform 108 may include one or morecomputing devices and/or other computer components (e.g., processors,memories, communication interfaces). In addition, and as illustrated ingreater detail below, client management computing platform 108 may beconfigured to generate, host, transmit, and/or otherwise provide one ormore web pages and/or other graphical user interfaces (which may, e.g.,cause one or more other computer systems to display and/or otherwisepresent the one or more web pages and/or other graphical userinterfaces). In some instances, the web pages and/or other graphicaluser interfaces generated by client management computing platform may beassociated with an internal portal provided by an organization, such asan error management portal provided by a financial institution. Such aportal may, for instance, provide employees of the financial institutionwith access to error information (e.g., error codes, types of errors,resolution methods, employees responsible for various errors, errorresolution times, or the like) and/or may provide employees of thefinancial institution with menus, controls, and/or other options toexecute various resolution actions (e.g., modify a system configuration,redirecting the error to another employee, initiate load balancing, orthe like). Such a portal may alternatively be an external portalprovided by an organization, such as an error management portal providedby a financial institution. Such a portal may, for instance, providecustomers of the financial institution with access to error information(e.g., types of errors, resolution methods, employees responsible forvarious errors, error resolution times, or the like).

Computing environment 100 also may include one or more networks, whichmay interconnect predictive error resolution computing platform 102,dynamic resource management computing platform 103, first virtualmachine host platform 104, second virtual machine host platform 105,first user device 106, second user device 107, and client managementcomputing platform 108. For example, computing environment 100 mayinclude a network 101 (which may interconnect, e.g., predictive errorresolution computing platform 102, dynamic resource management computingplatform 103, first virtual machine host platform 104, second virtualmachine host platform 105, first user device 106, second user device107, and client management computing platform 108).

In one or more arrangements, predictive error resolution computingplatform 102, dynamic resource management computing platform 103, firstvirtual machine host platform 104, second virtual machine host platform105, first user device 106, second user device 107, and clientmanagement computing platform 108, and/or the other systems included incomputing environment 100 may be any type of computing device capable ofreceiving a user interface, receiving input via the user interface, andcommunicating the received input to one or more other computing devices.For example, predictive error resolution computing platform 102, dynamicresource management computing platform 103, first virtual machine hostplatform 104, second virtual machine host platform 105, first userdevice 106, second user device 107, and client management computingplatform 108, and/or the other systems included in computing environment100 may, in some instances, be and/or include server computers, desktopcomputers, laptop computers, tablet computers, smart phones, or the likethat may include one or more processors, memories, communicationinterfaces, storage devices, and/or other components. As noted above,and as illustrated in greater detail below, any and/or all of predictiveerror resolution computing platform 102, dynamic resource managementcomputing platform 103, first virtual machine host platform 104, secondvirtual machine host platform 105, first user device 106, second userdevice 107, and client management computing platform 108 may, in someinstances, be special-purpose computing devices configured to performspecific functions.

Referring to FIG. 1B, predictive error resolution computing platform 102may include one or more processors 111, memory 112, and communicationinterface 113. A data bus may interconnect processor 111, memory 112,and communication interface 113. Communication interface 113 may be anetwork interface configured to support communication between predictiveerror resolution computing platform 102 and one or more networks (e.g.,network 101, or the like). Memory 112 may include one or more programmodules having instructions that when executed by processor 111 causepredictive error resolution computing platform 102 to perform one ormore functions described herein and/or one or more databases that maystore and/or otherwise maintain information which may be used by suchprogram modules and/or processor 111. In some instances, the one or moreprogram modules and/or databases may be stored by and/or maintained indifferent memory units of error resolution computing platform 102 and/orby different computing devices that may form and/or otherwise make uppredictive error resolution computing platform 102. For example, memory112 may have, store, and/or include a predictive error resolution module112 a, a predictive error resolution database 112 b, and a machinelearning engine 112 c. Predictive error resolution module 112 a may haveinstructions that direct and/or cause predictive error resolutioncomputing platform 102 to execute advanced predictive error resolutiontechniques, as discussed in greater detail below. Predictive errorresolution database 112 b may store information used by predictive errorresolution module 112 a and/or predictive error resolution computingplatform 102 in predictive error resolution and/or in performing otherfunctions. Machine learning engine 112 c may have instructions thatdirect and/or cause the predictive error resolution computing platform102 to perform predictive error resolution and to set, define, and/oriteratively refine optimization rules and/or other parameters used bythe predictive error resolution computing platform 102 and/or othersystems in computing environment 100.

Referring to FIG. 1C, dynamic resource management computing platform 103may include one or more processors 114, memory 115, and communicationinterface 116. A data bus may interconnect processor 114, memory 115,and communication interface 116. Communication interface 116 may be anetwork interface configured to support communication between dynamicresource management computing platform 103 and one or more networks(e.g., network 101, or the like). Memory 115 may include one or moreprogram modules having instructions that when executed by processor 114cause dynamic resource management computing platform 103 to perform oneor more functions described herein and/or one or more databases that maystore and/or otherwise maintain information which may be used by suchprogram modules and/or processor 114. In some instances, the one or moreprogram modules and/or databases may be stored by and/or maintained indifferent memory units of dynamic resource management computing platform103 and/or by different computing devices that may form and/or otherwisemake up dynamic resource management computing platform 103. For example,memory 115 may have, store, and/or include a dynamic resource managementmodule 115 a, a dynamic resource management database 115 b, and amachine learning engine 115 c. Dynamic resource management module 115 amay have instructions that direct and/or dynamic resource managementcomputing platform 103 to execute advanced dynamic resource managementtechniques, as discussed in greater detail below. Dynamic resourcemanagement database 115 b may store information used by dynamic resourcemanagement module 115 a and/or dynamic resource management computingplatform 103 in dynamic resource management and/or in performing otherfunctions. Machine learning engine 115 c may have instructions thatdirect and/or cause the dynamic resource management computing platform103 to perform dynamic resource management and to set, define, and/oriteratively refine optimization rules and/or other parameters used bythe dynamic resource management computing platform 103 and/or othersystems in computing environment 100.

FIGS. 2A-2L depict an illustrative event sequence for deploying aprocessing system for performing error resolution and dynamic systemconfiguration control that utilizes improved predictive error resolutionand dynamic resource management techniques in accordance with one ormore example embodiments. Referring to FIG. 2A, at step 201, firstvirtual machine host platform 104 may generate a first error log file.For example, the first virtual machine host platform 104 may attempt toprocess a plurality of payments and may experience a plurality of errorswhile doing so. For each of the plurality of errors, the first virtualmachine host platform 104 may determine an error code representative ofthe respective errors. In generating the first error log, the firstvirtual machine host platform 104 may compile a list of error codesuntil expiration of a predetermined period of time. In another example,the first virtual machine host platform 104 may compile a list of errorcodes until determining a predetermined amount of errors. For example,the first virtual machine host platform 104 may continue to generate thefirst error log until it determines ten error codes. The predeterminedamount of errors and the predetermined period of time may be determinedusing one or more machine learning algorithms and one or more machinelearning datasets. The predetermined amount of errors and thepredetermined period of time may also be configured by a user (such asan employee of a financial institution).

At step 202, first virtual machine host platform 104 may establish aconnection to predictive error resolution computing platform 102. Forexample, the first virtual machine host platform 104 may establish afirst wireless data connection to predictive error resolution computingplatform 102 to link the first virtual machine host platform 104 to thepredictive error resolution computing platform 102.

At step 203, the first virtual machine host platform 104 may send, tothe predictive error resolution computing platform 102, the first errorlog file generated at step 201. For example, the first virtual machinehost platform 104 may send, while the first wireless data connection isestablished, the first error log file. In some examples, the firstvirtual machine host platform 104 may send, at a predetermined interval,the first error log file. In other examples, the first virtual machinehost platform 104 may send, after determining that the first error logfile is complete, the first error log file.

At step 204, the predictive error resolution computing platform 102 mayreceive the first error log file from the first virtual machine hostplatform 104. For example, the predictive error resolution computingplatform 102 may receive, while the first wireless data connection isestablished and via the communication interface 113, the first error logfile.

Referring to FIG. 2B, at step 205, second virtual machine host platform105 may generate a second error log file. For example, the secondvirtual machine host platform 105 may attempt to process a plurality ofpayments and may experience a plurality of errors while doing so. Foreach of the plurality of errors, the second virtual machine hostplatform 105 may determine an error code representative of therespective errors. In generating the second error log, the secondvirtual machine host platform 105 may compile a list of error codesuntil expiration of a predetermined period of time. In another example,the first virtual machine host platform 104 may compile a list of errorcodes until determining a predetermined amount of errors. For example,the second virtual machine host platform 105 may continue to generatethe second error log until it determines ten error codes. Thepredetermined amount of errors and the predetermined period of time maybe determined using one or more machine learning algorithms and one ormore machine learning datasets. The predetermined amount of errors andthe predetermined period of time may also be configured by a user (suchas an employee of a financial institution). In some examples, the firstvirtual machine host platform 104 and the second virtual machine hostplatform 105 may be located in separate countries. Actions performed atstep 205 may be similar to those described above with regard to step201.

At step 206, second virtual machine host platform 105 may establish aconnection to predictive error resolution computing platform 102. Forexample, the second virtual machine host platform 105 may establish asecond wireless data connection to predictive error resolution computingplatform 102 to link the second virtual machine host platform 105 to thepredictive error resolution computing platform 102. Actions performed atstep 206 may be similar to those described above with regard to step202.

At step 207, the second virtual machine host platform 105 may send, tothe predictive error resolution computing platform 102, the second errorlog file generated at step 205. For example, the second virtual machinehost platform 105 may send, while the second wireless data connection isestablished, the second error log file. In some examples, the secondvirtual machine host platform 105 may send, at a predetermined interval,the second error log file. In other examples, the second virtual machinehost platform 105 may send, after determining that the second error logfile is complete, the second error log file. Actions performed at step207 may be similar to those described above with regard to step 203.

At step 208, the predictive error resolution computing platform 102 mayreceive the second error log file from the second virtual machine hostplatform 105. For example, the predictive error resolution computingplatform 102 may receive, while the second wireless data connection isestablished and via the communication interface 113, the second errorlog file. Actions performed at step 208 may be similar to thosedescribed above with regard to step 204.

Referring to FIG. 2C, at step 209, the predictive error resolutioncomputing platform 102 may generate an error lattice. For example, thepredictive error resolution computing platform 102 may combine, in realtime, error codes included in the first error log file and error codesincluded in the second error log file. In generating the error lattice,the predictive error resolution computing platform 102 may compile alist of error codes from multiple virtual machine host platforms (suchas first virtual machine host platform 104 and second virtual machinehost platform 105). In some examples, in generating the error lattice,the predictive error resolution computing platform 102 may generate aspreadsheet including a column corresponding to each of the multiplevirtual machine host platforms. Thus, by generating the error lattice,the predictive error resolution computing platform 102 may generate aspreadsheet containing a list of error codes corresponding to each ofthe multiple virtual machines. For example, Column A of the spreadsheetmay correspond to a first virtual machine and may list a plurality oferror codes corresponding to the first virtual machine. In this example,Column B of the spreadsheet may correspond to a second virtual machineand may list a plurality of error codes corresponding to the secondvirtual machine. The predictive error resolution computing platform 102may also determine, based on the error codes in the error lattice, erroroccurrences corresponding to the error codes and a timestamp of theerror occurrences. For example, the predictive error resolutioncomputing platform 102 may determine, based on one of the error codes,that a virtual machine host platform (such as the first virtual machinehost platform 104 or the second virtual machine host platform 105) isoverexerting, thus resulting in a memory leak, between 4 PM and 5 PM ona particular day.

At step 210, the predictive error resolution computing platform 102 mayidentify error code relationships using the error lattice. For example,the predictive error resolution computing platform 102 may determinethat multiple virtual machine host platforms (such as the first virtualmachine host platform 104 and the second virtual machine host platform105) are experiencing the same errors. For example, the predictive errorresolution computing platform 102 may determine that multiple virtualmachine host platforms (such as the first virtual machine host platform104 and the second virtual machine host platform 105) are outputting thesame error codes during similar time periods. In some examples, thepredictive error resolution computing platform 102 may determine thatthe multiple virtual machine host platforms are each being overexertedbetween 4 PM and 5 PM on Monday through Friday.

At step 211, the predictive error resolution computing platform 102 maydetermine a predicted error outcome. In determining the predicted erroroutcome, the predictive error resolution computing platform 102 maydetermine a consequence of previously accumulated error occurrences. Forexample, the predictive error resolution computing platform 102 maydetermine, based on the error code relationships, that a likelihood of aparticular error occurring exceeds a predetermined error outcomethreshold. As an example, if the predictive error resolution computingplatform 102 determined, at step 210, that the multiple virtual machinehost platforms are each being overexerted between 4 PM and 5 PM onMonday-Friday, the predictive error resolution computing platform 102may determine that the multiple virtual machine host platforms may beoverexerted the following Monday as well. If the predictive errorresolution computing platform 102 determines that the likelihood of thepredicted error outcome does not exceed the predetermined error outcomethreshold, the predictive error resolution computing platform 102 maycontinue to receive additional error logs until a predicted erroroutcome, exceeding the predetermined error outcome threshold, isdetermined. If the predictive error resolution computing platform 102determines that the likelihood of the predicted error outcome doesexceed the predetermined error outcome threshold, the predictive errorresolution computing platform 102 may proceed to step 212.

At step 212, predictive error resolution computing platform 102 maydetermine a system configuration update to be applied to the multiplevirtual machine host platforms. For example, if at step 211, thepredictive error resolution computing platform 102 determines apredicted error outcome indicating that the multiple virtual machinehost platforms may be overexerted tomorrow between 4 PM-5 PM, thepredictive error resolution computing platform 102 may determine asystem configuration update indicating that processing power of themultiple virtual machine host platforms should be increased during thistimeframe. In another example, the predictive error resolution computingplatform 102 may determine that additional computing resources (such asadditional virtual machine host platforms) should be added to thenetwork to reduce processing load on the multiple virtual machine hostplatforms during this timeframe. In yet another example, the predictiveerror resolution computing platform 102 may determine that requests tothe multiple virtual machine host platforms should be routed through aload balancing computing platform that may allocate requests to variousvirtual machine host platforms based on available processing power. Insome examples, the predictive error resolution computing platform 102may determine the system configuration update using one or more machinelearning algorithms and one or more machine learning datasets.

Referring to FIG. 2D, at step 213, the predictive error resolutioncomputing platform 102 may generate one or more commands directing thedynamic resource management computing platform 103 to distributerelevant portions of the system configuration update to each of themultiple virtual machine host platforms.

At step 214, the predictive error resolution computing platform 102 mayestablish a connection to dynamic resource management computing platform103. For example, the predictive error resolution computing platform 102may establish a third wireless data connection to dynamic resourcemanagement computing platform 103 to link the predictive errorresolution computing platform 102 to the dynamic resource managementcomputing platform 103.

At step 215, the predictive error resolution computing platform 102 maysend, to the dynamic resource management computing platform 103, the oneor more commands generated at step 213. For example, the predictiveerror resolution computing platform 102 may send, while the thirdwireless data connection is established and via the communicationinterface 113, the one or more commands.

At step 216, the dynamic resource management computing platform 103 mayreceive the one or more commands from the predictive error resolutioncomputing platform 102. For example, the dynamic resource managementcomputing platform 103 may receive, while the third wireless dataconnection is established and via the communication interface 116, theone or more commands directing the dynamic management computing platform103 to distribute relevant portions of the system configuration updateto each of the multiple virtual machine host platforms.

Referring to FIG. 2E, at step 217, the dynamic resource managementcomputing platform 103 may identify one or more virtual machine hostplatforms to which the system configuration update is applicable. Forexample, dynamic resource management computing platform 103 maydetermine that the system configuration update indicates that aprocessing capacity of both the first virtual machine host platform 104and the second virtual machine host platform 105 should be increased. Inthis example, the dynamic resource management computing platform 103 mayidentify the first virtual machine host platform 104 and the secondvirtual machine host platform 105 as virtual machine host platforms towhich the system configuration update is applicable. In some examples,the dynamic resource management computing platform 103 may determine thevirtual machine host platforms to which the system configuration updateis applicable based on a device identifier included in the one or morecommands received at step 216.

At step 218, dynamic resource management computing platform 103 mayestablish a connection to first virtual machine host platform 104. Forexample, the dynamic resource management computing platform 103 mayestablish a fourth wireless data connection to the first virtual machinehost platform 104 to link the dynamic resource management computingplatform 103 to the first virtual machine host platform 104.

At step 219, the dynamic resource management computing platform 103 maygenerate one or more commands directing the first virtual machine hostplatform 104 to perform system updates based on the system configurationupdate. For example, the dynamic resource management computing platform103 may generate one or more commands directing the first virtualmachine host platform 104 to increase processing speed using anoversubscription model. In another example, the dynamic resourcemanagement computing platform 103 may generate one or more commandsdirecting the first virtual machine host platform 104 to decreaseprocessing speed (to allow for increase capability by another virtualmachine host platform). In yet another example, the dynamic resourcemanagement computing platform 103 may generate one or more commandsdirecting the first virtual machine host platform 104 to turn on and toshare the processing load with the other virtual machine host platformsthat may already be turned on. In yet another example, the dynamicresource management computing platform 103 may generate one or morecommands directing the first virtual machine host platform 104 torecycle requests to avoid a backlog. In yet another example, the dynamicresource management computing platform 103 may generate one or morecommands directing the first virtual machine host platform 104 toincrease or decrease a data processing capacity.

At step 220, the dynamic resource management computing platform 103 maysend, to the first virtual machine host platform 104, the one or morecommands generated at step 219. For example, the dynamic resourcemanagement computing platform 103 may send, while the fourth wirelessdata connection is established and via the communication interface 116,the one or more commands directing the first virtual machine hostplatform 104 to perform system updates based on the system configurationupdate.

Referring to FIG. 2F, at step 221, the first virtual machine hostplatform 104 may receive the one or more commands from the dynamicresource management computing platform 103. For example, the firstvirtual machine host platform 104 may receive, while the fourth wirelessdata connection is established, the one or more commands directing thefirst virtual machine host platform 104 to perform system updates basedon the system configuration update.

At step 222, the first virtual machine host platform 104 may perform asystem update based on the commands received at step 221. In performingthe system update, the first virtual machine host platform 104 mayperform at least one of: turn on, turn off, increase data capacity,decrease data capacity, ramp up processing, or slow down processing.After performing the system update, the first virtual machine hostplatform 104 may send, to the dynamic resource management computingplatform 103, a configuration update confirmation notificationindicating that the system update has been performed.

At step 223, dynamic resource management computing platform 103 mayestablish a connection to second virtual machine host platform 105. Forexample, the dynamic resource management computing platform 103 mayestablish a fifth wireless data connection to the second virtual machinehost platform 105 to link the dynamic resource management computingplatform 103 to the second virtual machine host platform 105.

At step 224, the dynamic resource management computing platform 103 maygenerate one or more commands directing the second virtual machine hostplatform 105 to perform system updates based on the system configurationupdate. For example, the dynamic resource management computing platform103 may generate one or more commands directing the second virtualmachine host platform 105 to increase processing speed using anoversubscription model. In this example, the dynamic resource managementcomputing platform 103 may increase a quantity of requests routed to thesecond virtual machine host platform 105. In another example, thedynamic resource management computing platform 103 may generate one ormore commands directing the second virtual machine host platform 105 todecrease processing speed (to allow for increase capability by anothervirtual machine host platform). In yet another example, the dynamicresource management computing platform 103 may generate one or morecommands directing the second virtual machine host platform 105 to turnon and to share the processing load with the other virtual machine hostplatforms that may already be turned on. In yet another example, thedynamic resource management computing platform 103 may generate one ormore commands directing the second virtual machine host platform 105 torecycle requests to avoid a backlog. For example, rather thanaccumulating a queue of requests at the second virtual machine hostplatform 105, the second virtual machine host platform 105 may deletethe request, and the request may be routed to a different virtualmachine host platform. In yet another example, the dynamic resourcemanagement computing platform 103 may generate one or more commandsdirecting the second virtual machine host platform 105 to increase ordecrease a data processing capacity. Actions performed at step 224 maybe similar to those described above with regard to step 219.

Referring to FIG. 2G, at step 225, the dynamic resource managementcomputing platform 103 may send, to the second virtual machine hostplatform 105, the one or more commands generated at step 224. Forexample, the dynamic resource management computing platform 103 maysend, while the fifth wireless data connection is established and viathe communication interface 116, the one or more commands directing thesecond virtual machine host platform 105 to perform system updates basedon the system configuration update. Actions performed at step 225 may besimilar to those described above with regard to step 220.

At step 226, the second virtual machine host platform 105 may receivethe one or more commands from the dynamic resource management computingplatform 103. For example, the second virtual machine host platform 105may receive, while the fourth wireless data connection is established,the one or more commands directing the second virtual machine hostplatform 105 to perform system updates based on the system configurationupdate. Actions performed at step 226 may be similar to those describedabove with regard to step 221.

At step 227, the second virtual machine host platform 105 may perform asystem update based on the commands received at step 226. In performingthe system update, the second virtual machine host platform 105 mayperform at least one of: turn on, turn off, increase data capacity,decrease data capacity, ramp up processing, or slow down processing.After performing the system update, the second virtual machine hostplatform 105 may send, to the dynamic resource management computingplatform 103, a configuration update confirmation notificationindicating that the system update has been performed. Actions performedat step 227 may be similar to those described above with regard to step222.

In some instances, the dynamic resource management computing platform103 may determine whether an additional virtual machine host platformshould be directed to perform a system configuration update. If thedynamic resource management computing platform determines that anadditional virtual machine host platform should be directed to perform asystem configuration update, steps 223-227 may be repeated with regardto the additional virtual machine host platform. If the dynamic resourcemanagement computing platform 103 determines that an additional virtualmachine host platform should not be directed to perform a systemconfiguration update, the dynamic resource management computing platform103 may continue to step 228.

At step 228, the dynamic resource management computing platform 103 maygenerate an error map. For example, in generating the error map, thedynamic resource management computing platform 103 may determine, usingone or more machine learning algorithms and one or more machine learningdatasets, an operator corresponding to resolution of each of a pluralityof error codes. For example, in generating the error map, the dynamicresource management computing platform 103 may generate an indicationthat one of a database team member, a client solutions team member, amanagement team member, and the like is responsible for resolution of aparticular error code. In some examples, in generating the error map,the dynamic resource management computing platform 103 may generate aspreadsheet listing error codes in a first column and a correspondingoperator responsible for resolution of each of the error codes in asecond column. After generating the error map, the dynamic resourcemanagement computing platform 103 may store the error map using, forexample, the memory 115.

If the dynamic resource management computing platform 103 determinesthat a received error code is not listed in the error map, the dynamicresource management computing platform may determine an operatorassociated with resolution of the error code, and may then store the newerror code and the corresponding operator in the error map. In someexamples, the dynamic resource management computing platform 103 maydetermine the operator of a new error code by receiving input from anoperator attempting to resolve the new error code. Once this input isreceived, the dynamic resource management computing platform 103 maydetermine the operator of the new error code. In other examples, thedynamic resource management computing platform 103 may determine theoperator using one or more machine learning algorithms and one or moremachine learning datasets.

Referring to FIG. 2H, at step 229, the predictive error resolutioncomputing platform 102 may determine error information. For example, thepredictive error resolution computing platform 102 may determine that anactual error is occurring (as opposed to predicting a potential error).The predictive error resolution computing platform 102 may determine theactual error based on one of the first error log or the second errorlog. If an actual error is determined, the predictive error resolutioncomputing platform 102 may proceed to step 230. If the predictive errorresolution computing platform 102 does not determine an actual error,the predictive error resolution computing platform 102 may continue toreceive error logs, predict error outcomes, and determine systemconfiguration updates until an actual error is determined.

At step 230, the predictive error resolution computing platform 102 maysend the error information determined at step 229 to the dynamicresource management computing platform 103. The error information mayinclude one or more error codes corresponding to the actual errordetermined at step 229. For example, the predictive error resolutioncomputing platform 102 may send, while the third wireless dataconnection is established and via the communication interface 113, theerror information.

At step 231, dynamic resource management computing platform 103 mayreceive the error information from the predictive error resolutioncomputing platform 102. For example, the dynamic resource managementcomputing platform 103 may receive, while the third wireless dataconnection is established and via the communication interface 116, theerror information.

At step 232, the dynamic resource management computing platform 103 maydetermine operator interface information. For example, using the errorinformation received at step 231 and the error map generated at step228, the dynamic resource management computing platform 103 maydetermine the operator information. The dynamic resource managementcomputing platform 103 may determine the one or more error codesincluded in the error information. The dynamic resource managementcomputing platform 103 may then perform a lookup function to locate theone or more error codes within the error map. By performing the lookupfunction, the dynamic resource management computing platform 103 maydetermine one or more operators who may be tasked with resolving theactual error determined at step 229. Using an identity of the determinedoperator, the dynamic resource management computing platform 103 maygenerate operator information. In some instances, the dynamic resourcemanagement computing platform 103 may determine that the error map doesnot identify a particular error code. In this instance, the dynamicresource management computing platform 103 may determine one or moreoperators corresponding to the particular code using, for example, oneor more machine learning algorithms and one or more machine learningdatasets. After determining the one or more operators corresponding tothe particular code, the dynamic resource management computing platform103 may update the error map to identify the particular code and thecorresponding operators.

Referring to FIG. 2I, at step 233, the dynamic resource managementcomputing platform 103 may establish a connection to first user device106. For example, the dynamic resource management computing platform 103may establish a sixth wireless data connection to the first user device106 to link the dynamic resource management computing platform 103 tothe first user device 106.

At step 234, the dynamic resource management computing platform 103 maygenerate one or more hand off commands directing the first user device,corresponding to a first operator, to cause display of an operatorinterface corresponding to the first operator. For example, afterdetermining that the first operator is responsible for resolution of afirst error code in the error information, the dynamic resourcemanagement computing platform 103 may generate one or more hand offcommands directing the first user device 106, corresponding to the firstoperator, to notify the first operator of the first error code bygenerating the operator interface corresponding to the first operator.

At step 235, the dynamic resource management computing platform 103 maysend, to the first user device, the one or more hand off commandsgenerated at step 234. For example, the dynamic resource managementcomputing platform 103 may send, while the sixth wireless dataconnection is established, via the communication interface 116, andalong with the operator interface information, the one or more hand offcommands directing the first user device 106 to notify the firstoperator of the first error code by generating the operator interfacecorresponding to the first operator.

At step 236, the first user device 106 may receive the operatorinterface information and the one or more hand off commands from thedynamic resource management computing platform 103. For example, thefirst user device 106 may receive, while the sixth wireless dataconnection is established, the one or more hand off commands directingthe first user device 106 to notify the first operator of the firsterror code by generating the operator interface corresponding to thefirst operator

Referring to FIG. 2J, at step 237, the first user device 106 maygenerate and cause display of a first operator notification interface(e.g., based on the operator interface information and the one or morehand off commands directing the first user device 106 to notify thefirst operator of the first error code by generating the first operatorinterface). For example, the first user device 106 may display and/orotherwise present a graphical user interface similar to graphical userinterface 305, which is illustrated in FIG. 3. As seen in FIG. 3,graphical user interface 305 may include an indication of the error andan instruction for the first operator to resolve the error. For example,the graphical user interface 305 may indicate “Virtual Machine Overload;Database Expert: Please Resolve.” Although FIG. 3 shows an error and aninstruction to resolve the error, it should be understood that othersource data associated with the error may also be displayed via thegraphical user interface 305. For example, graphical user interface 305may include an option to delegate resolution of the error to anotheravailable operator, an option to show instructions for resolving theerror, an average resolution time for the error, and the like. In someexamples, the graphical user interface 305 may be displayed via anotification, an SMS message, an email message, and the like.

At step 238, the dynamic resource management computing platform 103 mayestablish a connection to second user device 107. For example, thedynamic resource management computing platform 103 may establish aseventh wireless data connection to the second user device 107 to linkthe dynamic resource management computing platform 103 to the seconduser device 107. Actions described at step 238 may be similar to thosedescribed above with regard to step 233.

At step 239, the dynamic resource management computing platform 103 maygenerate one or more hand off commands directing the second user device,corresponding to a second operator, to cause display of an operatorinterface corresponding to the second operator. For example, afterdetermining that the second operator is responsible for resolution of asecond error code in the error information, the dynamic resourcemanagement computing platform 103 may generate one or more hand offcommands directing the second user device 107, corresponding to thesecond operator, to notify the second operator of the second error codeby generating the operator interface corresponding to the secondoperator. Actions performed at step 239 may be similar to thosedescribed above with regard to step 235.

At step 240, the dynamic resource management computing platform 103 maysend, to the second user device, the one or more hand off commandsgenerated at step 239. For example, the dynamic resource managementcomputing platform 103 may send, while the seventh wireless dataconnection is established, via the communication interface 116, andalong with the operator interface information, the one or more hand offcommands directing the second user device 107 to notify the secondoperator of the second error code by generating the operator interfacecorresponding to the second operator. Actions performed at step 240 maybe similar to those described above with regard to step 235.

Referring to FIG. 2K, at step 241, the second user device 107 mayreceive the operator interface information and the one or more hand offcommands from the dynamic resource management computing platform 103.For example, the second user device 107 may receive, while the seventhwireless data connection is established, the one or more hand offcommands directing the second user device 107 to notify the secondoperator of the second error code by generating the operator interfacecorresponding to the second operator. Actions performed at step 241 maybe similar to those described above with regard to step 236.

At step 242, the second user device 107 may generate and cause displayof a second operator notification interface (e.g., based on the operatorinterface information and the one or more hand off commands directingthe second user device 107 to notify the second operator of the seconderror code by generating the second operator interface), different thanthe first operator notification interface. For example, the second userdevice 107 may display and/or otherwise present a graphical userinterface similar to graphical user interface 305, which is illustratedin FIG. 3 and is described above. In some instances, steps 238-242 maybe performed simultaneously with steps 233-237. For example, the dynamicresource management computing platform 103 may inform multiple operatorsof their responsibilities to resolve error codes at the same time. Inother instances, steps 233-237 may be performed after completion ofsteps 238-242. For example, the dynamic resource management computingplatform 103 may inform a first operator that there is a need to resolvea first error code. Once the dynamic resource management computingplatform 103 receives an indication that the first error code has beenresolved, the dynamic resource management computing platform 103 maynotify a second operator of the need to resolve a subsequent error code.Actions performed at step 242 may be similar to those described abovewith regard to step 237.

At step 243, the dynamic resource management computing platform 103 maygenerate an error correction hub. For example, based on the errorinformation and using at least one or more machine learning algorithmsand at least one or more machine learning datasets corresponding to theerror occurrences, the dynamic resource management computing platform103 may generate the error correction hub. For example, the errorcorrection hub may correspond to a graphical user interface similar tographical user interface 405, which is illustrated in FIG. 4. As seen inFIG. 4, graphical user interface 405 may include information associatedwith resolution of the error indicated in the error information, such asan error type, an operator tasked with resolution of the error, a methodfor resolving the error, an estimated resolution time, and the like. Itshould be understood that other information associated with resolutionof the error may be displayed via graphical user interface 405. In someexamples, if multiple operators are associated with resolution of theerror, the dynamic resource management computing platform 103 may modifythe graphical user interface 405 to remove information associated withresolution of a first error code, and to cause display of informationassociated with resolution of a second error code once the first errorcode has been resolved. In some examples, the dynamic resourcemanagement computing platform 103 may include information associatedwith resolution of multiple error codes at the same time in thegraphical user interface 405. In some instances, the informationdisplayed on the graphical user interface 405 may be determined usingone or more machine learning algorithms and one or more machine learningdatasets.

At step 244, the dynamic resource management computing platform 103 mayestablish a connection to client management computing platform 108. Forexample, the dynamic resource management computing platform 103 mayestablish an eighth wireless data connection to the client managementcomputing platform 108 to link the dynamic resource management computingplatform 103 to the client management computing platform 108.

Referring to FIG. 2L, at step 245, the dynamic resource managementcomputing platform 103 may generate one or more commands directing theclient management computing platform 108 to cause display of the errorcorrection hub generated at step 245.

At step 246, the dynamic resource management computing platform 103 maysend, to the client management computing platform 108, the one or morecommands generated at step 245. For example, the dynamic resourcemanagement computing platform 103 may send, while the eighth wirelessdata connection is established, via the communication interface 116, andalong with the error correction hub, the one or more commands directingthe client management computing platform 108 to cause display of theerror correction hub.

At step 247, the client management computing platform 108 may receivethe error correction hub and the one or more commands from the dynamicresource management computing platform 103. For example, the clientmanagement computing platform 108 may receive, while the eighth wirelessdata connection is established, the one or more commands directing theclient management computing platform 108 to cause display of the errorcorrection hub.

At step 248, the client management computing platform 108 may causedisplay of the error correction hub (e.g., based on information and thecommands from the dynamic resource management computing platform 103directing the client management computing platform 108 to cause displayof the error correction hub). For example, the client managementcomputing platform 108 may display and/or otherwise present a graphicaluser interface similar to graphical user interface 405, which isillustrated in FIG. 4 and is described above.

Subsequently, the example event sequence may end, and predictive errorresolution computing platform 102 and dynamic resource managementcomputing platform 103 may continue to predict errors and to causesystem configuration updates in a similar manner as discussed above(e.g., by comparing error log files, predicting error outcomes, andcausing system configuration updates to avoid the error outcomes) toimplement predictive error resolution and dynamic resource management.By operating in this way, predictive error resolution computing platform102 and dynamic resource management computing platform 103 may improveerror prediction, error resolution, and resource allocation for virtualmachines and/or other systems and devices included in computingenvironment 100. By performing error prediction based on error log filesfrom multiple virtual host machines, predictive error resolutioncomputing platform 102 may more accurately forecast potential errors andmay allow dynamic resource management computing platform 103 to moreeffectively manage system configuration of the virtual machines to avoiderror outcomes. If an error does occur, the dynamic resource managementcomputing platform 103 may facilitate the resolution process bydistributing different error codes included in the error information touser devices corresponding to operators responsible for resolution ofeach of the different error codes. This may increase the efficiency oferror resolution.

Although FIG. 2 depicts two virtual machine host platforms, two userdevices, and a single client management computing platform, it should beunderstood that this is merely exemplary and that any number of virtualmachine host platforms, user devices, and client management computingplatforms may be deployed in the system described.

FIG. 5 depicts an illustrative method for deploying a predictive errorresolution processing system that uses improved error prediction andmitigation techniques in accordance with one or more exampleembodiments. Referring to FIG. 5, at step 505, a computing platformhaving at least one processor, a communication interface, and memory mayreceive, from one or more virtual machine host platforms, one or moreerror log files. At step 510, the computing platform may generate, basedon the error log files, an error lattice. At step 515, after generatingthe error lattice, the computing platform may identify relationshipsbetween error codes in the error lattice. At step 520, the computingplatform may determine whether a predetermined error outcome thresholdhas been exceeded. If the predetermined error outcome threshold has notbeen exceeded, the computing platform may return to step 505 to receiveadditional error log files. If the predetermined error outcome thresholdhas been exceeded, the computing platform may proceed to step 525.

At step 525, the computing platform may determine a system configurationupdate to be applied to the one or more virtual host platforms. At step530, the computing platform may generate one or more systemconfiguration commands directing a dynamic resource management computingplatform to distribute relevant portions of the system configurationupdate to each of the one or more virtual machine host platforms. Atstep 535, the computing platform may send, to the dynamic resourcemanagement computing platform, the one or more system configurationcommands directing the dynamic resource management computing platform todistribute relevant portions of the system configuration update to eachof the one or more virtual host platforms. In some instances, by sendingthe one or more system configuration commands directing the dynamicresource management computing platform to distribute relevant portionsof the system configuration update to each of the one or more virtualhost platforms, the computing platform may cause the one or more virtualmachine host platforms to implement the system configuration update.

At step 540, the computing platform may determine whether an actualerror has been determined. If the computing platform determines that anactual error has not occurred, the computing platform may return to step505 to receive additional error logs. If the computing platform doesdetermine an actual error, the computing platform may proceed to step545. At step 545, in response to determining an actual error, thecomputing platform may send, to the dynamic resource managementcomputing platform, error information.

FIG. 6 depicts an illustrative method for deploying a dynamic systemconfiguration control processing system that uses improved techniques todynamically manage computing resources in accordance with one or moreexample embodiments. Referring to FIG. 6, at step 605, a computingplatform having at least one processor, a communication interface, andmemory may receive, from a predictive error resolution computingplatform 102, one or more system configuration commands directing thecomputing platform to distribute relevant portions of a systemconfiguration update. At step 610, in response to the one or more systemconfiguration commands directing the computing platform to distributerelevant portions of the system configuration update, the computingplatform may identify one or more virtual machine host platforms towhich the system configuration update is applicable. At step 615, thecomputing platform may generate virtual machine update commandsdirecting each of the one or more virtual machine host platforms towhich the system configuration update is applicable to perform system,updates based on the system configuration update. At step 620, aftergenerating the virtual machine update commands, the computing platformmay send, to each of the one or more virtual machine host platforms towhich the system configuration update is applicable, the virtual machineupdate commands.

At step 625, the computing platform may determine whether additionalvirtual machine host platforms should be directed to update a systemconfiguration. If the computing platform determines that additionalvirtual machines should be directed to update a system configuration,the computing platform may return to step 615 to generate additionalvirtual machine update commands. If the computing platform determinesthat additional virtual machines should not be directed to perform asystem configuration update, the computing platform may proceed to step630. At step 630, the computing platform may generate an error mapshowing correlations between error codes and operators responsible forresolution of each of the error codes.

At step 635, the computing platform may determine whether errorinformation was received, indicating an actual error. If errorinformation was not received, the computing platform may return to step605. If the computing platform did receive error information, thecomputing platform may proceed to step 640. At step 640, the computingplatform may determine, based on the error map and the errorinformation, operator interface information indicating one or moreoperators responsible for resolution of each error code included in theerror information. At step 645, the computing platform may generate oneor more hand off commands directing one or more user devices to causedisplay of an operator interface corresponding to each of the one ormore operators responsible for resolution of each error code included inthe error information. At step 650, after generating the one or morehand of commands, the computing platform may send, to the one or moreuser devices, the one or more hand off commands and the operatorinterface information.

At step 655, the computing platform may determine whether operatorinterface information should be sent to an additional user devicecorresponding to the one or more operators responsible for resolution ofeach error code included in the error information. If the computingplatform determines that operator interface information should be sentto an additional user device, the computing platform may return to step640. If the computing platform determines that operator interfaceinformation should not be sent to an additional user device, thecomputing platform may proceed to step 660. At step 660, the computingplatform may generate an error correction hub that presents informationcorresponding to resolution of the one or more error codes included inthe error information. At step 665, the computing platform may generateone or more error correction hub display commands directing a clientmanagement computing platform to cause display of the error correctionhub. At step 670, after generating the error correction hub displaycommands, the computing platform may send, along with the errorcorrection hub and to the client management computing platform, the oneor more error correction hub display commands.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A computing platform, comprising: at least oneprocessor; a communication interface communicatively coupled to the atleast one processor; and memory storing computer-readable instructionsthat, when executed by the at least one processor, cause the computingplatform to: receive, from multiple virtual machine host platforms, oneor more error log files identifying error codes corresponding to erroroccurrences associated with multiple applications running on themultiple virtual machine host platforms; generate, based on the one ormore error log files, an error lattice comprising an aggregation of theerror codes corresponding to the error occurrences; identify, using theerror lattice, relationships between the error codes corresponding tothe error occurrences, wherein identifying the relationships between theerror codes corresponding to the error occurrences comprises identifyinga match in the error codes during corresponding time periods for themultiple virtual machine host platforms; determine, based on therelationships between the error codes corresponding to the erroroccurrences, a predicted error outcome, wherein the predicted erroroutcome corresponds to the multiple virtual machine host platforms;determine, based on the predicted error outcome, a system configurationupdate to be applied to the multiple virtual machine host platforms,wherein the same configuration update is determined for each of themultiple virtual machine host platforms, and wherein determining thesystem configuration update comprises using an oversubscription model toincrease processing speed of one or more of the multiple virtual machinehost platforms or decreasing processing speed of the one or more of themultiple virtual machine host platforms; generate one or more commandsdirecting a dynamic resource management computing platform to distributerelevant portions of the system configuration update to each of themultiple virtual machine host platforms; and send, to the dynamicresource management computing platform, one or more commands directingthe dynamic resource management computing platform to distribute therelevant portions of the system configuration update to each of themultiple virtual machine host platforms, wherein sending the one or morecommands directing the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the multiple virtual machine host platforms causes the multiplevirtual machine host platforms to implement the system configurationupdate.
 2. The computing platform of claim 1, wherein the memory storesadditional computer-readable instructions that, when executed by the atleast one processor, further cause the computing platform to: establish,with a first virtual machine host platform of the multiple virtualmachine host platforms, a first wireless data connection; establish,with a second virtual machine host platform of the multiple virtualmachine host platforms, a second wireless data connection; andestablish, with the dynamic resource management computing platform, athird wireless data connection.
 3. The computing platform of claim 1,wherein: receiving the one or more error log files identifying the errorcodes corresponding to the error occurrences associated with multipleapplications running on the multiple virtual machine host platformscomprises receiving, via the communication interface, the one or moreerror log files identifying the error codes corresponding to the erroroccurrences associated with multiple applications running on themultiple virtual machine host platforms; and sending to the dynamicresource management computing platform the one or more commandsdirecting the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the multiple virtual machine host platforms comprises sending,via the communication interface and to the dynamic resource managementcomputing platform, the one or more commands directing the dynamicresource management computing platform to distribute the relevantportions of the system configuration update to each of the multiplevirtual machine host platforms.
 4. The computing platform of claim 1,wherein the memory stores additional computer-readable instructionsthat, when executed by the at least one processor, further cause thecomputing platform to: determine, prior to determining the systemconfiguration update to be applied to the multiple virtual machine hostplatforms, that the predicted error outcome exceeds a predeterminederror outcome threshold.
 5. The computing platform of claim 1, whereinthe memory stores additional computer-readable instructions that, whenexecuted by the at least one processor, further cause the computingplatform to: identify, based on the one or more error log files, anactual error; and send, after identifying the actual error and to thedynamic resource management computing platform, error informationcorresponding to the actual error.
 6. The computing platform of claim 1,wherein the system configuration update comprises increasing a datacapacity of at least one of the multiple virtual machine host platforms.7. The computing platform of claim 1, wherein generating the errorlattice comprising an aggregation of the error codes corresponding tothe error occurrences comprises generating the error lattice in realtime as the one or more error log files identifying error codescorresponding to error occurrences associated with multiple applicationsrunning on the multiple virtual machine host platforms are received. 8.The computing platform of claim 1, wherein determining the systemconfiguration update comprises: recycling requests to avoid a backlog,wherein recycling the requests comprises: identifying that the requestswill be added to a queue for subsequent processing at a first virtualmachine host platform of the multiple virtual machine host platforms,deleting the requests at the first virtual machine host platform, andsending the requests to a second virtual machine host platform of themultiple virtual machine host platforms, wherein the second virtualmachine host platform is configured to process the requests withoutadding the request to a queue of requests to be subsequently processed.9. A method comprising: at a computing platform comprising at least oneprocessor, a communication interface, and memory: receiving, frommultiple virtual machine host platforms, one or more error log filesidentifying error codes corresponding to error occurrences associatedwith multiple applications running on the multiple virtual machine hostplatforms; generating, based on the one or more error log files, anerror lattice comprising an aggregation of the error codes correspondingto the error occurrences; identifying, using the error lattice,relationships between the error codes corresponding to the erroroccurrences, wherein identifying the relationships between the errorcodes corresponding to the error occurrences comprises identifying amatch in the error codes during corresponding time periods for themultiple virtual machine host platforms; determining, based on therelationships between the error codes corresponding to the erroroccurrences, a predicted error outcome, wherein the predicted erroroutcome corresponds to the multiple virtual machine host platforms;determining, based on the predicted error outcome, a systemconfiguration update to be applied to the multiple virtual machine hostplatforms, wherein the same configuration update is determined for eachof the multiple virtual machine host platforms, and wherein determiningthe system configuration update comprises using an oversubscriptionmodel to increase processing speed of one or more of the multiplevirtual machine host platforms or decreasing processing speed of the oneor more of the multiple virtual machine host platforms; generating oneor more commands directing a dynamic resource management computingplatform to distribute relevant portions of the system configurationupdate to each of the multiple virtual machine host platforms; andsending, to the dynamic resource management computing platform, one ormore commands directing the dynamic resource management computingplatform to distribute the relevant portions of the system configurationupdate to each of the multiple virtual machine host platforms, whereinsending the one or more commands directing the dynamic resourcemanagement computing platform to distribute the relevant portions of thesystem configuration update to each of the multiple virtual machine hostplatforms causes the multiple virtual machine host platforms toimplement the system configuration update.
 10. The method of claim 9,further comprising: establishing, with a first virtual machine hostplatform of the multiple virtual machine host platforms, a firstwireless data connection; establishing, with a second virtual machinehost platform of the multiple virtual machine host platforms, a secondwireless data connection; and establishing, with the dynamic resourcemanagement computing platform, a third wireless data connection.
 11. Themethod of claim 9, further comprising: receiving the one or more errorlog files identifying the error codes corresponding to the erroroccurrences associated with multiple applications running on themultiple virtual machine host platforms comprises receiving, via thecommunication interface, the one or more error log files identifying theerror codes corresponding to the error occurrences associated withmultiple applications running on the multiple virtual machine hostplatforms; and sending to the dynamic resource management computingplatform the one or more commands directing the dynamic resourcemanagement computing platform to distribute the relevant portions of thesystem configuration update to each of the multiple virtual machine hostplatforms comprises sending, via the communication interface and to thedynamic resource management computing platform, the one or more commandsdirecting the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the multiple virtual machine host platforms.
 12. The method ofclaim 9, further comprising: determining, prior to determining thesystem configuration update to be applied to the multiple virtualmachine host platforms, that the predicted error outcome exceeds apredetermined error outcome threshold.
 13. The method of claim 9,further comprising: identifying, based on the one or more error logfiles, an actual error; and sending, after identifying the actual errorand to the dynamic resource management computing platform, errorinformation corresponding to the actual error.
 14. The method of claim9, wherein the system configuration update comprises increasing a datacapacity of at least one of the multiple virtual machine host platforms.15. The method of claim 9, wherein generating the error latticecomprising an aggregation of the error codes corresponding to the erroroccurrences comprises generating the error lattice in real time as theone or more error log files identifying error codes corresponding toerror occurrences associated with multiple applications running on themultiple virtual machine host platforms are received.
 16. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor, acommunication interface, and memory, cause the computing platform to:receive, from multiple virtual machine host platforms, one or more errorlog files identifying error codes corresponding to error occurrencesassociated with multiple applications running on the multiple virtualmachine host platforms; generate, based on the one or more error logfiles, an error lattice comprising an aggregation of the error codescorresponding to the error occurrences; identify, using the errorlattice, relationships between the error codes corresponding to theerror occurrences, wherein identifying the relationships between theerror codes corresponding to the error occurrences comprises identifyinga match in the error codes during corresponding time periods for themultiple virtual machine host platforms; determine, based on therelationships between the error codes corresponding to the erroroccurrences, a predicted error outcome, wherein the predicted erroroutcome corresponds to the multiple virtual machine host platforms;determine, based on the predicted error outcome, a system configurationupdate to be applied to the multiple virtual machine host platforms,wherein the same configuration update is determined for each of themultiple virtual machine host platforms, and wherein determining thesystem configuration update comprises using an oversubscription model toincrease processing speed of one or more of the multiple virtual machinehost platforms or decreasing processing speed of the one or more of themultiple virtual machine host platforms; generate one or more commandsdirecting a dynamic resource management computing platform to distributerelevant portions of the system configuration update to each of themultiple virtual machine host platforms; and send, to the dynamicresource management computing platform, one or more commands directingthe dynamic resource management computing platform to distribute therelevant portions of the system configuration update to each of themultiple virtual machine host platforms, wherein sending the one or morecommands directing the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the multiple virtual machine host platforms causes the multiplevirtual machine host platforms to implement the system configurationupdate.
 17. The one or more non-transitory computer-readable media ofclaim 16, wherein the memory stores additional instructions, that whenexecuted by the at least one processor, cause the at least one processorto: establish, with a first virtual machine host platform of themultiple virtual machine host platforms, a first wireless dataconnection; establish, with a second virtual machine host platform ofthe multiple virtual machine host platforms, a second wireless dataconnection; and establish, with the dynamic resource managementcomputing platform, a third wireless data connection.
 18. The one ormore non-transitory computer-readable media of claim 16, wherein thememory stores additional instructions, that when executed by the atleast one processor, cause the at least one processor to: receive theone or more error log files identifying the error codes corresponding tothe error occurrences associated with multiple applications running onthe multiple virtual machine host platforms comprises receiving, via thecommunication interface, the one or more error log files identifying theerror codes corresponding to the error occurrences associated withmultiple applications running on the multiple virtual machine hostplatforms; and send to the dynamic resource management computingplatform the one or more commands directing the dynamic resourcemanagement computing platform to distribute the relevant portions of thesystem configuration update to each of the multiple virtual machine hostplatforms comprises sending, via the communication interface and to thedynamic resource management computing platform, the one or more commandsdirecting the dynamic resource management computing platform todistribute the relevant portions of the system configuration update toeach of the multiple virtual machine host platforms.
 19. The one or morenon-transitory computer-readable media of claim 16, wherein the memorystores additional instructions, that when executed by the at least oneprocessor, cause the at least one processor to: determine, prior todetermining the system configuration update to be applied to themultiple virtual machine host platforms, that the predicted erroroutcome exceeds a predetermined error outcome threshold.
 20. The one ormore non-transitory computer-readable media of claim 16, wherein thememory stores additional instructions, that when executed by the atleast one processor, cause the at least one processor to: identify,based on the one or more error log files, an actual error; and send,after identifying the actual error and to the dynamic resourcemanagement computing platform, error information corresponding to theactual error.